Techbar

Description
 

We’re looking for an experienced Product Owner/Business Analyst to help our client build a graph-native intelligence and workflow platform in the FinTech domain. The platform is an intelligence layer that ingests documents and communications, structures data, and powers compliance workflows, approvals, and controlled AI recommendations.

You’ll act as the primary link between the client, business stakeholders and delivery team. You’ll translate complex business workflows and regulatory requirements into a clear, prioritized product backlog, own scope decisions, and make sure the platform matches the main principles defined during the Discovery phase.

Requirements
 

• 5+ years of experience as a Product Owner or Business Analyst on B2B SaaS or enterprise platform products. 
• Hands-on experience working with intelligent/AI-powered platforms defining scope, writing requirements, and collaborating with AI/ML teams. 
• Some experience in FinTech or another regulated financial services domain. 
• Experience working directly with senior business stakeholders to negotiate scope and priorities. Strong stakeholder management and communication skills.
• Experience in Agile/Scrum delivery (sprint planning, backlog refinement, story writing, acceptance criteria). 
• Fluent English. 
• Structured thinker who can map end-to-end workflows and identify edge cases (e.g. trusts, entities, multiple jurisdictions).

Responsibilities
 

• Own and prioritize the product backlog end-to-end: acceptance criteria, edge cases, definition of ready, all in place before engineering starts.
• Translate discovery findings, user interviews, and stakeholder input into clear epics, user stories, and acceptance criteria.
• Define and continuously refine MVP scope, balancing business value, regulatory requirements, and technical feasibility.
• Act as the primary point of contact between the client, business stakeholders and the delivery team.
• Collaborate with architecture and engineering on integration scope (KYC/ID providers, e-signature,CRM, etc.) without owning technical implementation decisions.
• Define success metrics and KPIs (e.g. onboarding cycle time, advisor capacity, compliance review time) and track value realization post-release.
• Support sprint planning, backlog refinement, release planning, and stakeholder demos/reviews.
• Maintain product documentation: scope, requirements traceability, decision log, and roadmap.

Nice to have
 

• Familiarity with financial services regulatory frameworks (e.g. MiFID II, GDPR, or local equivalents) and data residency/sovereignty requirements. 
• Conceptual understanding of relationship/graph-based data modeling for complex entity structures.
• Familiarity with digital ID/KYC providers, e-signature platforms, or PMS/OMS and custodian integrations. 
• Comfortable partnering with business analysts, architects, and UX designers across client and internal teams to convert ambiguous requirements into clear specifications.

About the role

We are looking for an experienced Backend Engineer with deep Python expertise to join an international engineering team building modern cloud-native solutions. The role focuses on designing scalable backend platforms, distributed services, and intelligent automation capabilities for enterprise-grade applications.

You will work closely with software architects, product stakeholders, and DevOps engineers to deliver secure, reliable, and high-performing systems.
 

What we're looking for

• 8+ years of commercial software engineering experience.
• Strong expertise in Python and modern backend development.
• Solid understanding of object-oriented programming, software design principles, and architectural patterns.
• Experience building scalable distributed applications and microservice-based solutions.
• Hands-on experience with modern Python frameworks such as FastAPI, Flask, or Django.
• Experience developing REST APIs and backend integrations.
• Strong knowledge of relational databases (PostgreSQL, SQL Server) and familiarity with NoSQL technologies.
• Practical experience with containerization and orchestration tools (Docker, Kubernetes).
• Experience working with cloud platforms, CI/CD pipelines, and automated deployment processes.
• Understanding of secure software development, application scalability, and system reliability.
• Ability to work independently in Agile teams.
• Strong communication skills and willingness to mentor other engineers.
   

Nice to have

• Experience with AI-powered applications.
• Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, or workflow automation.
• Exposure to cloud-native architectures and modern platform engineering.
   

Responsibilities

• Design and implement scalable backend services using Python.
• Develop distributed applications capable of handling high-load environments.
• Build and maintain APIs, integration services, and backend components.
• Participate in architecture discussions and technical decision-making.
• Improve application performance, reliability, observability, and security.
• Contribute to automated testing, CI/CD pipelines, and engineering best practices.
• Collaborate with cross-functional teams throughout the software development lifecycle.
• Review code, share knowledge, and support technical growth within the team.
   

What you'll get

• Long-term international project.
• Remote-first collaboration with an experienced engineering team.
• Opportunity to work with modern cloud technologies and AI-related solutions.
• Challenging technical tasks and ownership of engineering decisions.

We are looking for an experienced engineer to help the client build AI-ready enterprise data foundations in the Retail and FinTech domain, where complex business processes, governance, and connected data are critical for enterprise AI adoption.
  
You'll be focused on turning fragmented enterprise data into structured, connected knowledge that AI systems can understand and reason over. You’ll design and implement enterprise knowledge graphs, semantic data products, and business semantic layers that enable AI, analytics, and intelligent automation.

Responsibilities
 

• Design and implement enterprise knowledge graphs.
• Build semantic layers that create a common business understanding across enterprise systems.
• Model business entities, relationships, taxonomies, and ontologies.
• Develop semantic data products that can be consumed by AI applications, analytics platforms, and business users.
• Design enterprise metadata models and integrate with data catalogues.
• Connect structured and unstructured enterprise data into a unified semantic model.
• Collaborate with data architects, domain experts, business stakeholders, and AI teams to capture business meaning.
• Support enterprise AI initiatives by creating machine-readable business context.
• Ensure governance, lineage, metadata quality, and semantic consistency across data assets.
• Integrate semantic models with modern data platforms and cloud ecosystems.

Key requirements
 

• Strong experience designing and implementing Knowledge Graphs.
• Experience with semantic technologies, including RDF, OWL, SKOS, SPARQL, or similar standards.
• Experience with graph databases such as Neo4j, Amazon Neptune, Stardog, GraphDB, or equivalent.
• Understanding of enterprise metadata management and data catalogues.
• Experience building semantic models, ontologies, taxonomies, and controlled vocabularies.
• Knowledge of enterprise data architecture and data modelling.
• Strong SQL skills and experience integrating data from multiple enterprise systems.
• Experience working with cloud data platforms (Azure, AWS, or GCP).
• Familiarity with AI, LLMs, RAG architectures, or enterprise AI solutions is a strong advantage.

Nice to Have

• Experience with semantic search or enterprise search.
• Experience with Master Data Management (MDM).
• Knowledge of Data Mesh or Data Products.
• Experience with enterprise governance frameworks.
• Experience supporting AI agent or GenAI implementations.